Multi-objective optimization of the dressing parameters in fine cylindrical grinding

english

2019

volume 65, issue 2

diamond roller dressers   dressing parameters   fine cylindrical grinding   multi-objective optimization

Univerza v Ljubljani, Fakulteta za strojništvo
(Obvezni izvod spletne publikacije)

The optimum conditions for dressing grinding wheels determined and recommended in the literature are valid only for particular types and tools of dressing and grinding. In this paper, an attempt has been made to optimize the dressing process parameters in fine cylindrical grinding. To define the optimum values of the dressing process variables (radial feed rate of diamond roller dresser frd, dressing speed ratio qd, dress-out time td, diamond roller dresser grit size/grinding wheel grit size ratio qg, type of synthetic diamonds and direction of dressing), a multi-objective optimization has been performed based on a genetic algorithm. In the capacity of the optimization parameter, a generalized geometric-mean utility function has been chosen, which appears to be a complex indicator characterizing the roughness and accuracy of the ground surface, the grinding wheel lifetime and the manufacturing net costs of the grinding operation. The optimization problem has been solved in the following sequence: 1) a model of the generalized utility function has been created reflecting the complex effect of the dressing system parameters; 2) the optimum conditions of uni-directional and counter-directional dressing of aluminium oxide grinding wheels by experimental diamond roller dressers of synthetic diamonds of AC32 and AC80 types and different grit size at which the generalized utility function has a maximum have been determined; 3) a Pareto optimum solution has been found (frd=0.2 mm/min; qd=0.8; td=4.65 s; qg=2.56), which guarantees the best combination between the roughness and the deviation from cylindricity of the ground surface, the grinding wheel lifetime and the manufacturing net costs of the grinding operation.

11.03.2019